“There are already cases of totally autonomous AIs with black boxes, and they’ve either failed a lot of times or succeeded in some ways. But that’s not necessarily, I think, where the future is going,” Kornfeld says. “I think the real shift of thinking about AI is to see it as a teammate, as a co-worker, as a somebody that’s going to do a task for you and can add value in that area. But to kind of pit human and AI portfolio managers against each other isn’t, necessarily, my viewpoint of it. I don’t think AI is going to replace the portfolio manager. I think it’s going to redefine what the portfolio manager can do in a day.”
What Kornfeld envisions is a system where one PM is effectively running a whole team of AI agents, each with their own tasks, collaborating with each-other and the PM to reproduce the work of human PM teams. The human, with a greater capacity for reasoning than any AI large language model has yet demonstrated, remains in the driver’s seat. Key processes of research, analytics, and risk identification can be conducted by the AI agents. The scutwork of compliance filings and trade hygiene are also easily handed off to AI agents. Moreover, the speed with which these agents can gather information on key events like an earnings report might give the AI-human PM team an advantage.
For PMs and advisors considering adding AI agents into their workflows, Kornfeld stresses that framing them as colleagues is key. As with any colleague, you have to train them, anticipate mistakes, give feedback, and correct them. The promise of AI is not in its ability to conduct each task perfectly on the first try, but in its capacity to learn fast and adapt to feedback. Expecting perfection, he says, is unrealistic and will lead to disappointment. Putting in the work to train and sculpt an AI that a PM can trust to do their work will pay dividends.
Part of that feedback work, Kornfeld notes, is establishing essential guardrails and security controls for an AI tool. Oversight is essential for success. For example, PMs must clearly delineate between the exploration of a hypothetical trade in a research request and the execution of a real trade. Doing that feedback work may mean that adding an AI tools is actually detrimental to efficiency in the short-term, much as the way adding a new inexperienced teammate can be. Just as a new graduate may be turned into a talented PM, an AI tool requires input and feedback to be made valuable and additive to efficiency.
Kornfeld cites his own former professor at Stanford University Jeremy Utley, who teaches on AI and design thinking. Utley has long promulgated the idea of viewing AI as a teammate and reframing the adoption of AI as man vs. machine into a narrative of collaboration and specialization.
